Numerical modeling of subglacial erosion and sediment transport beneath the Laurentide Ice Sheet
Bibliographic record
Abstract
Towards this goal, we present a subglacial process model that incorporates mechanisms for sediment production, entrainment, transport, and deposition. An abrasion law based on Hallet’s model and a quarrying law dependent on basal water pressure and bed roughness are used to calculate bedrock erosion. The incorporation of loose debris in the basal ice is modeled by regelation intrusion and basal freeze-on, depending on the thermal condition and the availability of water at the base. The entrained debris is subsequently transported along the ice sheet’s internal velocity field and vertically mixed through a diffusion equation that accounts for folding and thrust faulting. The inclusion of vertical mixing lowers the basal debris concentration and allows more regelation entrainment. Soft bed deformation is included as an advective component within the subglacial sediment, the rheology of which is assumed to be weakly non-linear. Deposition occurs whenever the basal ice is debris-laden and the melting rate exceeds the entrainment rate. The model is coupled to the MUN 3D GSM, which includes a newly developed subglacial hydrology module. The GSM itself has been subject to Bayesian calibration for North American and Eurasian deglaciation and thus a probabilistic ensemble of deglacial chronologies is available. With this calibrated ensemble, we compare the range of calculated sediment thickness fields and cumulative erosion over the last glacial cycle against the present-day pattern of glacigenic sediment and the geological estimates of glacial erosion over North America, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".